Tackling The Cancer Epidemic Using AI: A Hands On Session in Breast Cancer Detection

Tackling The Cancer Epidemic Using AI: A Hands On Session in Breast Cancer Detection

September 30, 2019
| 1640 views

In recent times, breast cancer has become the most common type of cancer affecting women worldwide accounting for 25% of all cancer cases and affected 3.5 million people in 2017-18. Early diagnosis in these cases significantly increases the chances of survival. The key challenge in cancer detection is how to classify tumours into malignant or benign. Research indicates that most experienced physicians can diagnose cancer with 79% accuracy while using artificial intelligence based diagnosis, it is possible to achieve 91% accuracy.

The session first half focussed on Wisconsin Diagnostic Cancer (WDBC) & Invasive Ductal Carcinoma (IDC) datasets insights, Visualization of Dataset, feature selection and why they are chosen and how the physical parameters are translated into a dataset. In the second part, the focus was on Feature Selection and CNN, random forest based classification of cancer as malignant or benign followed by the optimised the deployment strategy & cost estimation

WISCONSIN DIAGNOSTIC BREAST CANCER (WDBC):

The samples consist of visually assessed nuclear features of Fine Needle Aspirates (FNAs) taken from patients. Attributes 3 to 11 were used to form a 9-dimensional vector which was used to obtain a neural network to discriminate between benign and malignant samples. Cross-validation was used to project the accuracy of the diagnostic algorithm.

Field

Attribute

1

Sample code number

2

Class: 2 for benign, 4 for malignant

3

Clump Thickness

4

Uniformity of Cell Size

5

Uniformity of Cell Shape

6

Marginal Adhesion

7

Single Epithelial Cell Size

8

Bare Nuclei

9

Bland Chromatin

10

Normal Nucleoli

11

Mitoses

INVASIVE DUCTAL CARCINOMA (IDC)

Invasive Ductal Carcinoma dataset originally consisted of 162 slide images, scanned at 40x. From that, 277,524 patches of 50×50 pixels (which is converted to 32 x 32pixels to fit the model architecture) were extracted, including, 198,738 IDC negative examples & 78,786 IDC positive examples. Each image in the dataset is labelled based on the following parameters:

Patient ID: 10253_idx5

x-coordinate of the crop: 1,351

y-coordinate of the crop: 1,101

Class label: 0 (0 indicates no IDC while 1 indicates IDC)

CNN Architecture used:

Used exclusively 3×3 CONV filters, similar to VGGNet

Stacked multiple 3×3 CONV filters on top of each other prior to performing max-pooling (again, similar to VGGNet

But unlike VGGNet, used depth-wise separable convolution rather than standard convolution layer

The next session will be focused on the Prognostic System. This recently put into clinical practice, is a method that predicts when the cancer is likely to recur in patients that have had their cancers excised. This gives the physician and the patient better information with which to plan treatment and may eliminate the need for a prognostic surgical procedure. The novel feature of the predictive approach is the ability to handle cases for which cancer has not recurred as well as cases for which cancer has recurred at a specific time.